SG-CPG: Severity-Gated Central Pattern Generators for Adaptive Quadruped Locomotion under Continuous Actuator Degradation

📅 2026-09-22
📈 Citations: 0
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🤖 AI Summary
研究提出SG-CPG方法,通过引入两个严重度驱动的门控机制,解决四足机器人在持续执行器退化下的适应性运动问题。
📝 Abstract
An animal with a weakened limb does not necessarily switch its gait, instead it unloads the affected limb, re-coordinates the remaining limbs, and scales its response with injury severity. This graded adaptation allows locomotion to persist despite partial loss of limb strength, rather than requiring a discrete transition between healthy and failed. Inspired by this behavior, we propose SG-CPG, a central pattern generator (CPG) for quadruped locomotion under continuous actuator degradation. SG-CPG preserves a frozen healthy CPG policy and introduces two severity-driven gates: a residual gate that re-coordinates all four legs and an amplitude gate that progressively shortens the weakened leg's stride as degradation increases. We emulate progressive degradation through two mechanisms: lowering the joint torque ceiling (ceiling mechanism) and scaling its low-level controller gains (gain mechanism), representing distinct forms of actuator weakening. Our simulations on a Unitree Go2 show that SG-CPG maintains a trot gait with 100% survival across an omnidirectional command schedule under 95% joint strength loss while tracking commands within 8%. Under a lowered torque ceiling, removing either severity path, the residual's severity observation or the amplitude gate, raises clipping at the weakened joint from 4.4% to 13.6% and 26.3% of steps at an 80% loss. On a real Go2, SG-CPG survives 28 of 29 forward and turning trials with up to 93% calf torque degradation. These results show that severity-gated adaptation can extend a healthy locomotion policy to progressive actuator degradation without treating the fault as a discrete failure.
Problem

Research questions and friction points this paper is trying to address.

Continuous Actuator Degradation
Adaptive Locomotion
Quadruped
Innovation

Methods, ideas, or system contributions that make the work stand out.

Severity-Gated
Central Pattern Generator
Adaptive Locomotion
Actuator Degradation
Residual and Amplitude Gates
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Adarsh Kumar Kosta
Adarsh Kumar Kosta
PhD student, C-BRIC, Purdue University
Neuromorphic computingDeep learningSpiking Neural networksEvent-based vision
K
Kaushik Roy
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906, USA